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CRangeBearingKFSLAM2D.h
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1/* +---------------------------------------------------------------------------+
2 | Mobile Robot Programming Toolkit (MRPT) |
3 | http://www.mrpt.org/ |
4 | |
5 | Copyright (c) 2005-2016, Individual contributors, see AUTHORS file |
6 | See: http://www.mrpt.org/Authors - All rights reserved. |
7 | Released under BSD License. See details in http://www.mrpt.org/License |
8 +---------------------------------------------------------------------------+ */
9#ifndef CRangeBearingKFSLAM2D_H
10#define CRangeBearingKFSLAM2D_H
11
18
20#include <mrpt/utils/bimap.h>
21
26#include <mrpt/maps/CLandmark.h>
30
32
33namespace mrpt
34{
35 namespace slam
36 {
37 /** An implementation of EKF-based SLAM with range-bearing sensors, odometry, and a 2D (+heading) robot pose, and 2D landmarks.
38 * The main method is "processActionObservation" which processes pairs of action/observation.
39 *
40 * The following pages describe front-end applications based on this class:
41 * - http://www.mrpt.org/Application:2d-slam-demo
42 * - http://www.mrpt.org/Application:kf-slam
43 *
44 * \sa CRangeBearingKFSLAM \ingroup metric_slam_grp
45 */
47 public bayes::CKalmanFilterCapable<3 /* x y yaw */, 2 /* range yaw */, 2 /* x y */, 3 /* Ax Ay Ayaw */>
48 // <size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE, size typename kftype = double>
49 {
50 public:
51 typedef mrpt::math::TPoint2D landmark_point_t; //!< Either mrpt::math::TPoint2D or mrpt::math::TPoint3D
52
53 CRangeBearingKFSLAM2D( ); //!< Default constructor
54 virtual ~CRangeBearingKFSLAM2D(); //!< Destructor
55 void reset(); //!< Reset the state of the SLAM filter: The map is emptied and the robot put back to (0,0,0).
56
57 /** Process one new action and observations to update the map and robot pose estimate. See the description of the class at the top of this page.
58 * \param action May contain odometry
59 * \param SF The set of observations, must contain at least one CObservationBearingRange
60 */
64
65 /** Returns the complete mean and cov.
66 * \param out_robotPose The mean & 3x3 covariance matrix of the robot 2D pose
67 * \param out_landmarksPositions One entry for each of the M landmark positions (2D).
68 * \param out_landmarkIDs Each element[index] (for indices of out_landmarksPositions) gives the corresponding landmark ID.
69 * \param out_fullState The complete state vector (3+2M).
70 * \param out_fullCovariance The full (3+2M)x(3+2M) covariance matrix of the filter.
71 * \sa getCurrentRobotPose
72 */
74 mrpt::poses::CPosePDFGaussian &out_robotPose,
75 std::vector<mrpt::math::TPoint2D> &out_landmarksPositions,
76 std::map<unsigned int,mrpt::maps::CLandmark::TLandmarkID> &out_landmarkIDs,
77 mrpt::math::CVectorDouble &out_fullState,
78 mrpt::math::CMatrixDouble &out_fullCovariance
79 ) const;
80
81 /** Returns the mean & 3x3 covariance matrix of the robot 2D pose.
82 * \sa getCurrentState
83 */
85 mrpt::poses::CPosePDFGaussian &out_robotPose ) const;
86
87 /** Returns a 3D representation of the landmarks in the map and the robot 3D position according to the current filter state.
88 * \param out_objects
89 */
91
92 /** Load options from a ini-like file/text
93 */
95
96 /** The options for the algorithm
97 */
99 {
100 /** Default values */
102
103 void loadFromConfigFile(const mrpt::utils::CConfigFileBase &source,const std::string &section) MRPT_OVERRIDE; // See base docs
104 void dumpToTextStream(mrpt::utils::CStream &out) const MRPT_OVERRIDE; // See base docs
105
106 mrpt::math::CVectorFloat stds_Q_no_odo; //!< A 3-length vector with the std. deviation of the transition model in (x,y,phi) used only when there is no odometry (if there is odo, its uncertainty values will be used instead); x y: In meters, phi: radians (but in degrees when loading from a configuration ini-file!)
107 float std_sensor_range, std_sensor_yaw; //!< The std. deviation of the sensor (for the matrix R in the kalman filters), in meters and radians.
108 float quantiles_3D_representation; //!< Default = 3
109 bool create_simplemap; //!< Whether to fill m_SFs (default=false)
110
111 // Data association:
114 double data_assoc_IC_chi2_thres; //!< Threshold in [0,1] for the chi2square test for individual compatibility between predictions and observations (default: 0.99)
115 TDataAssociationMetric data_assoc_IC_metric; //!< Whether to use mahalanobis (->chi2 criterion) vs. Matching likelihood.
116 double data_assoc_IC_ml_threshold;//!< Only if data_assoc_IC_metric==ML, the log-ML threshold (Default=0.0)
117
118 };
119
120 TOptions options; //!< The options for the algorithm
121
122
123 /** Save the current state of the filter (robot pose & map) to a MATLAB script which displays all the elements in 2D
124 */
126 const std::string &fil,
127 float stdCount=3.0f,
128 const std::string &styleLandmarks = std::string("b"),
129 const std::string &stylePath = std::string("r"),
130 const std::string &styleRobot = std::string("r") ) const;
131
132
133 /** Information for data-association:
134 * \sa getLastDataAssociation
135 */
137 {
139 Y_pred_means(0,0),
140 Y_pred_covs(0,0)
141 {
142 }
143
144 void clear() {
145 results.clear();
146 predictions_IDs.clear();
147 newly_inserted_landmarks.clear();
148 }
149
150 // Predictions from the map:
153
154 /** Map from the 0-based index within the last observation and the landmark 0-based index in the map (the robot-map state vector)
155 Only used for stats and so. */
156 std::map<size_t,size_t> newly_inserted_landmarks;
157
158 // DA results:
160 };
161
162 /** Returns a read-only reference to the information on the last data-association */
164 return m_last_data_association;
165 }
166
167 protected:
168
169 /** @name Virtual methods for Kalman Filter implementation
170 @{
171 */
172
173 /** Must return the action vector u.
174 * \param out_u The action vector which will be passed to OnTransitionModel
175 */
176 void OnGetAction( KFArray_ACT &out_u ) const;
177
178 /** Implements the transition model \f$ \hat{x}_{k|k-1} = f( \hat{x}_{k-1|k-1}, u_k ) \f$
179 * \param in_u The vector returned by OnGetAction.
180 * \param inout_x At input has \f[ \hat{x}_{k-1|k-1} \f] , at output must have \f$ \hat{x}_{k|k-1} \f$ .
181 * \param out_skip Set this to true if for some reason you want to skip the prediction step (to do not modify either the vector or the covariance). Default:false
182 */
184 const KFArray_ACT &in_u,
185 KFArray_VEH &inout_x,
186 bool &out_skipPrediction
187 ) const;
188
189 /** Implements the transition Jacobian \f$ \frac{\partial f}{\partial x} \f$
190 * \param out_F Must return the Jacobian.
191 * The returned matrix must be \f$V \times V\f$ with V being either the size of the whole state vector (for non-SLAM problems) or VEH_SIZE (for SLAM problems).
192 */
193 void OnTransitionJacobian( KFMatrix_VxV &out_F ) const;
194
195 /** Only called if using a numeric approximation of the transition Jacobian, this method must return the increments in each dimension of the vehicle state vector while estimating the Jacobian.
196 */
198
199
200 /** Implements the transition noise covariance \f$ Q_k \f$
201 * \param out_Q Must return the covariance matrix.
202 * The returned matrix must be of the same size than the jacobian from OnTransitionJacobian
203 */
204 void OnTransitionNoise( KFMatrix_VxV &out_Q ) const;
205
206 /** This is called between the KF prediction step and the update step, and the application must return the observations and, when applicable, the data association between these observations and the current map.
207 *
208 * \param out_z N vectors, each for one "observation" of length OBS_SIZE, N being the number of "observations": how many observed landmarks for a map, or just one if not applicable.
209 * \param out_data_association An empty vector or, where applicable, a vector where the i'th element corresponds to the position of the observation in the i'th row of out_z within the system state vector (in the range [0,getNumberOfLandmarksInTheMap()-1]), or -1 if it is a new map element and we want to insert it at the end of this KF iteration.
210 * \param in_S The full covariance matrix of the observation predictions (i.e. the "innovation covariance matrix"). This is a M*O x M*O matrix with M=length of "in_lm_indices_in_S".
211 * \param in_lm_indices_in_S The indices of the map landmarks (range [0,getNumberOfLandmarksInTheMap()-1]) that can be found in the matrix in_S.
212 *
213 * This method will be called just once for each complete KF iteration.
214 * \note It is assumed that the observations are independent, i.e. there are NO cross-covariances between them.
215 */
217 vector_KFArray_OBS &out_z,
218 vector_int &out_data_association,
219 const vector_KFArray_OBS &in_all_predictions,
220 const KFMatrix &in_S,
221 const vector_size_t &in_lm_indices_in_S,
222 const KFMatrix_OxO &in_R
223 );
224
226 const vector_size_t &idx_landmarks_to_predict,
227 vector_KFArray_OBS &out_predictions
228 ) const;
229
230 /** Implements the observation Jacobians \f$ \frac{\partial h_i}{\partial x} \f$ and (when applicable) \f$ \frac{\partial h_i}{\partial y_i} \f$.
231 * \param idx_landmark_to_predict The index of the landmark in the map whose prediction is expected as output. For non SLAM-like problems, this will be zero and the expected output is for the whole state vector.
232 * \param Hx The output Jacobian \f$ \frac{\partial h_i}{\partial x} \f$.
233 * \param Hy The output Jacobian \f$ \frac{\partial h_i}{\partial y_i} \f$.
234 */
236 const size_t &idx_landmark_to_predict,
237 KFMatrix_OxV &Hx,
238 KFMatrix_OxF &Hy
239 ) const;
240
241 /** Only called if using a numeric approximation of the observation Jacobians, this method must return the increments in each dimension of the vehicle state vector while estimating the Jacobian.
242 */
244 KFArray_VEH &out_veh_increments,
245 KFArray_FEAT &out_feat_increments ) const;
246
247
248 /** Computes A=A-B, which may need to be re-implemented depending on the topology of the individual scalar components (eg, angles).
249 */
251
252 /** Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of the sensor.
253 * \param out_R The noise covariance matrix. It might be non diagonal, but it'll usually be.
254 */
256
257 /** This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the number of covariance landmark predictions to be made.
258 * For example, features which are known to be "out of sight" shouldn't be added to the output list to speed up the calculations.
259 * \param in_all_prediction_means The mean of each landmark predictions; the computation or not of the corresponding covariances is what we're trying to determined with this method.
260 * \param out_LM_indices_to_predict The list of landmark indices in the map [0,getNumberOfLandmarksInTheMap()-1] that should be predicted.
261 * \note This is not a pure virtual method, so it should be implemented only if desired. The default implementation returns a vector with all the landmarks in the map.
262 * \sa OnGetObservations, OnDataAssociation
263 */
265 const vector_KFArray_OBS &in_all_prediction_means,
266 vector_size_t &out_LM_indices_to_predict ) const;
267
268 /** If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element".
269 * \param in_z The observation vector whose inverse sensor model is to be computed. This is actually one of the vector<> returned by OnGetObservations().
270 * \param out_yn The F-length vector with the inverse observation model \f$ y_n=y(x,z_n) \f$.
271 * \param out_dyn_dxv The \f$F \times V\f$ Jacobian of the inv. sensor model wrt the robot pose \f$ \frac{\partial y_n}{\partial x_v} \f$.
272 * \param out_dyn_dhn The \f$F \times O\f$ Jacobian of the inv. sensor model wrt the observation vector \f$ \frac{\partial y_n}{\partial h_n} \f$.
273 *
274 * - O: OBS_SIZE
275 * - V: VEH_SIZE
276 * - F: FEAT_SIZE
277 *
278 * \note OnNewLandmarkAddedToMap will be also called after calling this method if a landmark is actually being added to the map.
279 */
281 const KFArray_OBS & in_z,
282 KFArray_FEAT & out_yn,
283 KFMatrix_FxV & out_dyn_dxv,
284 KFMatrix_FxO & out_dyn_dhn ) const;
285
286 /** If applicable to the given problem, do here any special handling of adding a new landmark to the map.
287 * \param in_obsIndex The index of the observation whose inverse sensor is to be computed. It corresponds to the row in in_z where the observation can be found.
288 * \param in_idxNewFeat The index that this new feature will have in the state vector (0:just after the vehicle state, 1: after that,...). Save this number so data association can be done according to these indices.
289 * \sa OnInverseObservationModel
290 */
292 const size_t in_obsIdx,
293 const size_t in_idxNewFeat );
294
295
296 /** This method is called after the prediction and after the update, to give the user an opportunity to normalize the state vector (eg, keep angles within -pi,pi range) if the application requires it.
297 */
299
300 /** @}
301 */
302
303
304 void getLandmarkIDsFromIndexInStateVector(std::map<unsigned int,mrpt::maps::CLandmark::TLandmarkID> &out_id2index) const
305 {
306 out_id2index = m_IDs.getInverseMap();
307 }
308
309 protected:
310
311 /** Set up by processActionObservation */
313
314 /** Set up by processActionObservation */
316
317 /** The mapping between landmark IDs and indexes in the Pkk cov. matrix: */
319
320 /** The sequence of all the observations and the robot path (kept for debugging, statistics,etc) */
322
323 TDataAssocInfo m_last_data_association; //!< Last data association
324 }; // end class
325 } // End of namespace
326} // End of namespace
327
328#endif
Virtual base for Kalman Filter (EKF,IEKF,UKF) implementations.
mrpt::aligned_containers< KFArray_OBS >::vector_t vector_KFArray_OBS
This class stores a sequence of <Probabilistic Pose,SensoryFrame> pairs, thus a "metric map" can be t...
CArrayNumeric is an array for numeric types supporting several mathematical operations (actually,...
Definition: CArrayNumeric.h:26
A numeric matrix of compile-time fixed size.
Column vector, like Eigen::MatrixX*, but automatically initialized to zeros since construction.
Definition: types_math.h:65
Declares a class that represents a Probability Density function (PDF) of a 2D pose .
An implementation of EKF-based SLAM with range-bearing sensors, odometry, and a 2D (+heading) robot p...
void getCurrentRobotPose(mrpt::poses::CPosePDFGaussian &out_robotPose) const
Returns the mean & 3x3 covariance matrix of the robot 2D pose.
void OnObservationModel(const vector_size_t &idx_landmarks_to_predict, vector_KFArray_OBS &out_predictions) const
Implements the observation prediction .
void OnNewLandmarkAddedToMap(const size_t in_obsIdx, const size_t in_idxNewFeat)
If applicable to the given problem, do here any special handling of adding a new landmark to the map.
void getLandmarkIDsFromIndexInStateVector(std::map< unsigned int, mrpt::maps::CLandmark::TLandmarkID > &out_id2index) const
void OnGetObservationsAndDataAssociation(vector_KFArray_OBS &out_z, vector_int &out_data_association, const vector_KFArray_OBS &in_all_predictions, const KFMatrix &in_S, const vector_size_t &in_lm_indices_in_S, const KFMatrix_OxO &in_R)
This is called between the KF prediction step and the update step, and the application must return th...
void OnTransitionNoise(KFMatrix_VxV &out_Q) const
Implements the transition noise covariance .
mrpt::maps::CSimpleMap m_SFs
The sequence of all the observations and the robot path (kept for debugging, statistics,...
void OnPreComputingPredictions(const vector_KFArray_OBS &in_all_prediction_means, vector_size_t &out_LM_indices_to_predict) const
This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the...
void OnTransitionModel(const KFArray_ACT &in_u, KFArray_VEH &inout_x, bool &out_skipPrediction) const
Implements the transition model .
virtual ~CRangeBearingKFSLAM2D()
Destructor.
mrpt::obs::CSensoryFramePtr m_SF
Set up by processActionObservation.
void processActionObservation(mrpt::obs::CActionCollectionPtr &action, mrpt::obs::CSensoryFramePtr &SF)
Process one new action and observations to update the map and robot pose estimate.
void getAs3DObject(mrpt::opengl::CSetOfObjectsPtr &outObj) const
Returns a 3D representation of the landmarks in the map and the robot 3D position according to the cu...
void OnObservationJacobiansNumericGetIncrements(KFArray_VEH &out_veh_increments, KFArray_FEAT &out_feat_increments) const
Only called if using a numeric approximation of the observation Jacobians, this method must return th...
void loadOptions(const mrpt::utils::CConfigFileBase &ini)
Load options from a ini-like file/text.
void saveMapAndPath2DRepresentationAsMATLABFile(const std::string &fil, float stdCount=3.0f, const std::string &styleLandmarks=std::string("b"), const std::string &stylePath=std::string("r"), const std::string &styleRobot=std::string("r")) const
Save the current state of the filter (robot pose & map) to a MATLAB script which displays all the ele...
void reset()
Reset the state of the SLAM filter: The map is emptied and the robot put back to (0,...
void OnNormalizeStateVector()
This method is called after the prediction and after the update, to give the user an opportunity to n...
void OnInverseObservationModel(const KFArray_OBS &in_z, KFArray_FEAT &out_yn, KFMatrix_FxV &out_dyn_dxv, KFMatrix_FxO &out_dyn_dhn) const
If applicable to the given problem, this method implements the inverse observation model needed to ex...
TOptions options
The options for the algorithm.
mrpt::utils::bimap< mrpt::maps::CLandmark::TLandmarkID, unsigned int > m_IDs
The mapping between landmark IDs and indexes in the Pkk cov.
const TDataAssocInfo & getLastDataAssociation() const
Returns a read-only reference to the information on the last data-association.
void OnObservationJacobians(const size_t &idx_landmark_to_predict, KFMatrix_OxV &Hx, KFMatrix_OxF &Hy) const
Implements the observation Jacobians and (when applicable) .
mrpt::obs::CActionCollectionPtr m_action
Set up by processActionObservation.
void getCurrentState(mrpt::poses::CPosePDFGaussian &out_robotPose, std::vector< mrpt::math::TPoint2D > &out_landmarksPositions, std::map< unsigned int, mrpt::maps::CLandmark::TLandmarkID > &out_landmarkIDs, mrpt::math::CVectorDouble &out_fullState, mrpt::math::CMatrixDouble &out_fullCovariance) const
Returns the complete mean and cov.
TDataAssocInfo m_last_data_association
Last data association.
void OnTransitionJacobian(KFMatrix_VxV &out_F) const
Implements the transition Jacobian .
void OnSubstractObservationVectors(KFArray_OBS &A, const KFArray_OBS &B) const
Computes A=A-B, which may need to be re-implemented depending on the topology of the individual scala...
CRangeBearingKFSLAM2D()
Default constructor.
void OnTransitionJacobianNumericGetIncrements(KFArray_VEH &out_increments) const
Only called if using a numeric approximation of the transition Jacobian, this method must return the ...
void OnGetObservationNoise(KFMatrix_OxO &out_R) const
Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of ...
void OnGetAction(KFArray_ACT &out_u) const
Must return the action vector u.
mrpt::math::TPoint2D landmark_point_t
Either mrpt::math::TPoint2D or mrpt::math::TPoint3D.
This class allows loading and storing values and vectors of different types from a configuration text...
This is a virtual base class for sets of options than can be loaded from and/or saved to configuratio...
This base class is used to provide a unified interface to files,memory buffers,..Please see the deriv...
Definition: CStream.h:39
A bidirectional version of std::map, declared as bimap<KEY,VALUE> and which actually contains two std...
Definition: bimap.h:29
TDataAssociationMetric
Different metrics for data association, used in mrpt::slam::data_association For a comparison of both...
TDataAssociationMethod
Different algorithms for data association, used in mrpt::slam::data_association.
std::vector< int32_t > vector_int
Definition: types_simple.h:23
std::vector< size_t > vector_size_t
Definition: types_simple.h:25
#define MRPT_OVERRIDE
C++11 "override" for virtuals:
Definition: mrpt_macros.h:28
struct OBS_IMPEXP CActionCollectionPtr
struct OBS_IMPEXP CSensoryFramePtr
struct OPENGL_IMPEXP CSetOfObjectsPtr
Definition: CSetOfObjects.h:23
This is the global namespace for all Mobile Robot Programming Toolkit (MRPT) libraries.
Lightweight 2D point.
std::map< size_t, size_t > newly_inserted_landmarks
Map from the 0-based index within the last observation and the landmark 0-based index in the map (the...
mrpt::math::CMatrixTemplateNumeric< kftype > Y_pred_covs
void dumpToTextStream(mrpt::utils::CStream &out) const MRPT_OVERRIDE
This method should clearly display all the contents of the structure in textual form,...
void loadFromConfigFile(const mrpt::utils::CConfigFileBase &source, const std::string &section) MRPT_OVERRIDE
This method load the options from a ".ini"-like file or memory-stored string list.
TDataAssociationMetric data_assoc_IC_metric
Whether to use mahalanobis (->chi2 criterion) vs. Matching likelihood.
double data_assoc_IC_chi2_thres
Threshold in [0,1] for the chi2square test for individual compatibility between predictions and obser...
bool create_simplemap
Whether to fill m_SFs (default=false)
mrpt::math::CVectorFloat stds_Q_no_odo
A 3-length vector with the std. deviation of the transition model in (x,y,phi) used only when there i...
double data_assoc_IC_ml_threshold
Only if data_assoc_IC_metric==ML, the log-ML threshold (Default=0.0)
The results from mrpt::slam::data_association.



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